Improvements in and relating to missile targeting

ABSTRACT

A method of targeting a missile. A plurality of images of a target, taken from a plurality of viewpoints, are received. Features in the images characteristic of the target are identified. Data representing the characteristic features are provided to the missile to enable the missile to identify, using the characteristic features, the target in images of the environment of the missile obtained from an imager included in the missile.

FIELD OF THE INVENTION

This invention relates to the field of missile targeting. The inventionrelates in particular to apparatus and methods for identifying a targetfor a guided or homing missile.

BACKGROUND ART

Several different ways of identifying a target for a guided or homingmissile are known in the art. A simple approach is to provide themissile with a location of the target, known from intelligence reports,surveillance imagery or other surveillance activity or from publicsources. The missile is then guided to that location, typically inmodern systems using its own onboard guidance systems, inertial and/orsatellite-based. However, that approach is limited to targets that arefixed, or at least reliably known to be in a particular location at aparticular time. Even in those cases, targeting can be relativelycoarse, at least for small, locally mobile targets, delivering themissile only to the approximate location of the target. Moreover, if theintelligence reports or other sources prove to be inaccurate, orout-of-date, the missile is delivered to a location from which thetarget has left or where it has never been. Another common approach,particularly for relatively short-range missiles, is to aim the missiletowards the target and to rely on radar or ladar for guidance to thetarget in the final phase of flight. That approach is adequate insituations in which significant returns to the radar or ladar are fromthe target and no other objects, or from several objects all of whichare targets, but it is not good when a target is surrounded by otherobjects that provide strong returns.

Although guidance to a specified location remains useful in getting themissile to the vicinity of the target, more precise missile targeting toa specific target usually requires control by a human having visualcontact with the target. For example, in semi-active laser targeting, aperson with a direct line-of-sight to the target illuminates it with alaser of a preselected wavelength. The incoming missile includes asensor, typically a quadrant sensor, which detects reflections of thelaser wavelength from the target and the missile steers itself towardsthe source of those reflections. In another example, the missileincludes an onboard camera or other imaging system, which relays images,from the missile in flight, to a remote human operator, whether in anaircraft or on the ground. The operator reviews the images andidentifies the target. The operator then either steers the missile tothe target or provides sufficient information to the missile for it tolock onto the target and steer itself towards it. In a variant of thisapproach, the images are provided by a camera or other imaging system onboard an ISTAR-UAV circling the target or operated by a human on theground.

However, human intervention in the targeting process—an “operator in theloop”—has many drawbacks. In the case of semi-active laser targeting,for example, the operator is required to have a line-of-sight to thetarget until close to the moment of detonation of the missile. Clearly,that is potentially extremely hazardous for the operator. Even where theoperator is remote, communication delays and interruptions can causeproblems. The operator must be trained to be sufficiently skilled intarget recognition and remain vigilant in his or her monitoring of theimages. There is a significant risk of error.

In recent years, there has therefore been much interest in automatictargeting of missiles to specific targets. For example, it is known toprovide a missile with image processing software including a database oftarget shapes, so that images provided by the missile's imaging systemare processed and matches to the target shape, if any, are identified.As space and power on board a missile are limited, a more commonapproach is to provide the image processing software to the remote humanoperator, so that the images are pre-processed before they are presentedto the operator. Specifically, the image-processing software identifiesobjects in the images that are possible matches to the target shapes inthe database, and highlights those objects in the images presented tothe operator. That helps the operator to spot potential targets, but thefinal identification and designation of an object as a target is by theoperator.

In another variant, images of the target are provided to the missile byan ISTAR-UAV, human on the ground, or other source, and image processingsoftware on board the missile looks for objects in an image stream fromthe missile's own camera that match the image provided to the missile.This approach can require significant bandwidth between the source ofimages and the missile, which is often not available, and may stillrequire an operator in the loop to make final targeting decisions, asdescribed above.

A further difficulty is that missiles usually have only limited on-boardresources, for example processors and power supplies, and soresource-intensive processes (e.g. complex image processing) are notpossible.

It would be advantageous to provide improved apparatus and methods ofmissile targeting in which the above-described disadvantages areeliminated or at least ameliorated.

SUMMARY

Briefly and in general terms, the present invention provides apparatusdirected towards improving targeting of missiles by comparingcharacteristic features of the target and the image in the field of viewof the seeker.

The invention provides, in a first aspect, a method of targeting amissile, the method comprising:

receiving a plurality of images of a target taken from a plurality ofviewpoints;

identifying in the images features characteristic of the target;

providing data representing the characteristic features to the missileto enable the missile to identify, using the characteristic features,the target in images of the environment of the missile obtained from animager included in the missile.

The invention also provides, in a second aspect, a method of targeting amissile, the method comprising:

causing the missile to receive data representing features characteristicof a target, the characteristic features having been identified in aplurality of images of the target taken from a plurality of viewpoints;

the missile identifying, using the characteristic features, the targetin images of the environment of the missile obtained from an imagerincluded in the missile.

The invention also provides, in a third aspect, a missile comprising:

a telecommunications receiver for receiving data representing featurescharacteristic of a target, the characteristic features having beenidentified in a plurality of images of the target taken from a pluralityof viewpoints;

an imager for obtaining images of the environment of the missile;

a data processor configured to identify, using the characteristicfeatures, the target in images of the environment of the missileobtained from the imager.

It will be appreciated that features described in relation to one aspectof the present invention can be incorporated into other aspects of thepresent invention. For example, an apparatus of the invention canincorporate any of the features described in this disclosure withreference to a method, and vice versa. Moreover, additional embodimentsand aspects will be apparent from the following description, drawings,and claims. As can be appreciated from the foregoing and followingdescription, each and every feature described herein, and each and everycombination of two or more of such features, and each and everycombination of one or more values defining a range, are included withinthe present disclosure provided that the features included in such acombination are not mutually inconsistent. In addition, any feature orcombination of features or any value(s) defining a range may bespecifically excluded from any embodiment of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

Example embodiments of the invention will now be described by way ofexample only and with reference to the accompanying drawings, of which:

FIG. 1 is a flowchart showing steps of an example of a method accordingto the invention;

FIG. 2 is a flowchart showing a step of the method of FIG. 1 in moredetail, namely extraction of SIFT features and descriptors; and

FIG. 3 is another step of the method of FIG. 1, namely matching featuresbetween images.

For convenience and economy, the same reference numerals are used indifferent figures to label identical or similar elements of the enginesshown.

DETAILED DESCRIPTION

Embodiments are described herein in the context of approaches to improvemethods of targeting missiles.

Those of ordinary skill in the art will realise that the followingdetailed description is illustrative only and is not intended to be inany way limiting. Other embodiments of the present invention willreadily suggest themselves to such skilled persons having the benefit ofthis disclosure. Reference will be made in detail to implementations asillustrated in the accompanying drawings.

As previously stated, the first aspect is directed to a method oftargeting a missile. A plurality of images of a target, taken from aplurality of viewpoints, are received. Features characteristic of thetarget are identified in the images. Data representing thecharacteristic features are provided to the missile to enable themissile to identify, using the characteristic features, the target inimages of the environment of the missile obtained from an imagerincluded in the missile.

The method may include the step of collecting the images of the target.The images may be collected using, for example, a hand-held camera ormobile phone. The plurality of viewpoints may be overlapping viewpoints.

It may be that the features that are characteristic of the target areregions of the target in which in the image of the target provide arapid change in contrast, that is, a change in contrast greater than aselected threshold value. It may be that the features that arecharacteristic of the target are corner regions of the target. It may bethat features that are characteristic of the target are identified usinga scale-invariant feature transform (SIFT) algorithm.

The identification of the characteristic features may include the stepof generating rescaled versions of at least one of the images of thetarget. The rescaling may, for example, be achieved by deleting ormultiplying pixels.

The identification of the characteristic features may include the stepof smoothing the rescaled image versions. The smoothing may be carriedout using a Gaussian kernel.

The identification of the characteristic features may include the stepof calculating difference images between the smoothed, rescaled imageversions. The difference images may be calculated by taking theDifference of Gaussians between the smoothed, rescaled image versions.

The identification of the characteristic features may include the stepof finding extrema in the difference images.

The identification of the characteristic features may include the stepof assigning an orientation to each extremum. The orientation may beassigned to the extremum using gradients in the greyscale value ofpixels in the difference images.

The identification of the characteristic features may include the stepof generating a vector describing the extrema, for example comprisingthe orientation of the extrema.

The method may include the step of matching characteristic featuresacross two or more of the plurality of images. The matching may includethe step of calculating a distance, for example a Gaussian-weightedEuclidean distance, between characteristic features being matched. Itmay be that the matching is carried out pairwise between all of thecharacteristic features in a first of the plurality of images and all ofthe characteristic features in a second of the plurality of images (i.e.every characteristic feature in the first image is matched with everycharacteristic feature in the second image). The matching may includeassessing the quality of the match against a statistical significancetest. The matching may include assessing the quality of the match bycalculating the best fit similarity transform between characteristicfeatures in a first of the plurality of images and characteristicfeatures in a second of the plurality of images. The similaritytransform may be a translation of the centroid of the characteristicfeatures in the respective images, a rescaling of the characteristicfeatures in the respective images, or a rotation of the characteristicfeatures in the respective images, or a combination of all three.

The method may include the step of forming a view cluster includingcharacteristic features from two or more of the corresponding images.The view cluster may be formed by selecting a reference image and one ormore other images from the plurality of images, matching characteristicfeatures in the other image(s) to characteristic features in thereference image, and adding to the reference image further, unmatched,characteristic features from the other image(s) that have not beenpreviously identified as characteristic features in the reference image.

The method may include the step of creating a model of the target fromthe characteristic features in the view clusters.

The imager included in the missile may be a seeker.

The method may include the step of identifying features characteristicof the target in the images of the environment of the missile. Themethod may include the step of matching the characteristic features inthe images of the environment of the missile to characteristic featuresin the view clusters or target model. The matching may include the stepof calculating a distance, for example a Gaussian-weighted Euclideandistance, between characteristic features being matched. It may be thatthe matching is carried out pairwise between all of the characteristicfeatures in the images of the environment of the missile and all of thecharacteristic features in the view clusters or target model. Thematching may include assessing the quality of the match against astatistical significance test. The matching may include assessing thequality of the match by calculating the best fit similarity transformbetween characteristic features in the images of the environment of themissile and characteristic features in one or more images of the targetor view clusters or a target model. The similarity transform may be atranslation of the centroid of the characteristic features in therespective images, a rescaling of the characteristic features in therespective images, or a rotation of the characteristic features in therespective images, or a combination of all three.

The method may include the step of estimating the location and pose ofthe target in the images of the environment of the missile.

As previously stated, the second aspect is directed to a method oftargeting a missile. The missile is caused to receive data representingidentifying features characteristic of a target, the identifyingfeatures having been identified in a plurality of images of the targettaken from a plurality of viewpoints. The missile identifies, using theidentifying features, the target in images of the environment of themissile obtained from an imager included in the missile.

As previously stated, the third aspect is directed to a missile. Themissile comprises a telecommunications receiver for receiving datarepresenting identifying features characteristic of a target, theidentifying features having been identified in a plurality of images ofthe target taken from a plurality of viewpoints. The missile comprisesan imager for obtaining images of the environment of the missile. Themissile comprises a data processor configured to identify, using theidentifying features, the target in images of the environment of themissile obtained from the imager.

A flowchart describing a first example method is shown in FIG. 1. In amodel-creation phase, reconnaissance images of the target are collectedfrom a plurality of (preferably overlapping) viewpoints (step A1). SIFTfeatures and descriptors are extracted from the images (step A2), as ameans to find visual correspondences between the reference images of thetarget and the seeker image of the target scene. However, first the SIFTfeatures are matched across the reconnaissance images (step A3). Thatenables the formation of view clusters (step A4); in this step, asmaller number of key images are enhanced by inclusion of SIFT pointsfrom neighbouring images. The view cluster images thereby each provide a“summary” of a plurality of overlapping images taken from adjacentviewpoints.

In steps A2 (of the model-creation phase) and B2 (of the targetidentification phase), in this example, the SIFT features anddescriptors are extracted by the method shown in FIG. 2. The input image10 is a reconnaissance image and the output of the SIFT detector is alist of 2D SIFT points 20 on the image each associated to a vector ofdescriptors 30. These are known as keypoints and they provide a means oflocal image description. Each reconnaissance image is used to generateseveral corresponding images at different resolutions.

There are several stages to the detection of keypoints. In the firststage the image is rescaled (sub-sampled) (step 40) over several octavesand smoothed (step 50) by convolving the image with a Gaussian kernel ofdifferent widths s=σ², where σ denotes the standard deviation and thevariance of the Gaussian kernel. Each octave represents a singlerescaling of the image at a different resolution. Within each octave s,the image is smoothed by a Gaussian kernel of different widths (k^(m)σ)where k=√{square root over (2)}. The second stage involves taking thedifference of Gaussians (DoG) between the rescaled images (step 60) andlocating the interest points at which the DoG values are extrema withrespect to both the spatial coordinates in the image domain and thescale level in the pyramid (step 70). An accurate keypoint localisationis obtained using a quadratic fit to the nearby data. Steps are thentaken to eliminate points that have low contrast or occur along an edgesince edge points are poorly localised. The third stage involvesassigning one or more orientations to each keypoint based on local imagegradients. The fourth stage involves taking the image gradients (step80) and transforming them into a vector of feature descriptors (30) thatallows for changes in local shape distortion and change in illumination.

The view clusters are used to form a target feature model (step A5).

In a target identification phase, the missile seeker generates an image(step B1). SIFT features and descriptors are extracted from the image(B2). Features are matched between the target feature mold generated instep A5 from the view clusters and the features of the seeker image(step B3). The location and pose of the target in the seeker image areestimated from the matching (step B4).

In the first stage (re-scaling) (step 40), higher resolution images aregenerated by replacing each pixel in the original image with severalpixels in the higher-resolution image; for example, an image at twicethe resolution of the original is generated by replacing each pixel inthe original image with a square array of four identical pixels in thehigher-resolution image. Lower resolution images are generated byremoving pixels. For example, an image at half the resolution of theoriginal is generated by removing three out of every four pixels in eachsquare array of pixels in the original image. In this example, imagesare generated at two-times, one-half times and one-quarter times theresolution of the original image.

All image are smoothed using a Gaussian kernel (step 50). The Gaussiankernel is represented as

${G( {x,{y;s}} )} = {\frac{1}{2\pi \; s}e^{{- {({x^{2} + y^{2}})}}/{({2\; s})}}}$

where s represents the width s=σ². Each rescaling generates a subsampledimage in the image pyramid known as the SIFT scale space imagerepresentation. This consists of N octaves defined by two parameters sand G. Let f be the input image. Each octave is an ordered set of s+3images such that

L(x,y;k ^(m)σ)=G(x,y;k ^(m)σ)*f _(i)(x,y),k=√{square root over (2)}

where L(.) is the convolved image, G(.) is the Gaussian kernel, f_(i),the ith sub-sample of f, m=0, 1, . . . , s+2 and i=1, . . . , N.

The second stage is to take pixel-by-pixel differences in intensitybetween convolved adjacent images producing the difference-of-Gaussiansin each octave of scale space (step 60). Mathematically, this isrepresented by the Difference-of-Gaussians operator DoG as

DoG(x,y;s)=L(x,y;s+Δs)−L(x,y;s)

In this example, that is the differences between (i) the original imageand the double resolution image, (ii) the original image and thesmoothed half-resolution image, and (iii) the smoothed half-resolutionimage and the quarter-resolution image. This process generatesdifference images.

The next step is to look for extrema (step 70), that is maxima andminima, in the difference (DoG) images. An extremum is a pixel in thedifference image having an intensity above a chosen threshold value (thepixel is then a maximum) or below a chosen threshold value (the pixel isthen a minimum). Persistent extrema, that is extrema occurring in all ormost of the difference images, are designated SIFT points 20, and theirco-ordinates in the image recorded, as described in Lowe (U.S. Pat. No.6,711,293 B1).

The location of the extrema is refined by considering a quadratic fit tonearby data. Many extrema exhibit small contrast values and these shouldbe eliminated since they are not relevant to the description of theimage. Two filters are used, one to discard the keypoints with smallcontrast and the other to remove points that occur along edges.

Each keypoint is now coded as a triplet (x, y, σ) whose gradient hasmagnitude m and orientation θ given by

${m( {x,y,\sigma} )} = \sqrt{( {{L( {{x + 1},y,\sigma} )} - {L( {{x - 1},y,\sigma} )}} )^{2} + ( {{L( {x,{y + 1},\sigma} )} - {L( {x,{y - 1},\sigma} )}} )^{2}}$$\mspace{20mu} {{\theta ( {x,y,\sigma} )} = {\arctan ( \frac{{L( {x,{y + 1},\sigma} )} - {L( {x,{y - 1},\sigma} )}}{{L( {{x + 1},y,\sigma} )} - {L( {{x - 1},y,\sigma} )}} )}}$

The third step of the algorithm is to assign orientations to thekeypoints. To do this the histogram of gradient orientations isaccumulated over a region about each keypoint. The gradient directionand magnitude of the Gaussian pyramid images is calculated using theformulae above (step 80). The orientation of the keypoint is located bylooking for peaks in the histogram of gradient orientations. A keypointmay be assigned more than one orientation. If it is, then two identicaldescriptors are added to the database with different orientations. Ahistogram with 36 bin entries is created into which the gradientorientations are added covering the 360 degree range of orientations.Each sample is weighted by the gradient magnitude and a Gaussianweighting circular window with a σ that is 1.5 times that of the scaleof the keypoint. The peaks in the orientation histogram correspond tothe dominant directions of local gradients. The highest peak in thehistogram is localised and a quadratic function is fit to the 3histogram values closest to each peak to interpolate the peak positionto greater accuracy.

The sampling grid is then rotated to the main orientation of eachkeypoint using the interpolated value of the peak in the histogram. Thegrid is a 4×4 array of 4×4 sample cells of a 8 bin orientationhistogram. Each bin in the histogram corresponds to 8 “compassdirections” N, NE, etc. Taken together, the local histograms computed atall the 4×4 grid points and with 8 quantised directions lead to afeature descriptor vector with 128 entries. This resulting descriptor isreferred to as the SIFT descriptor 30.

The histogram of gradient orientation samples is also weighted by thegradient magnitude and a Gaussian filter with a standard deviation of ½the feature window size. To avoid boundary effects, each sample isaccumulated into neighbouring bins weighted by a factor (1−d) in alldimensions, where d is the centre of the bin measured in units of binspacing.

The resulting descriptor vector is normalised to a unit vector bydividing all entries by the magnitude of the vector. This makes thedescriptor insensitive to moderate changes in illumination.

So, in this example, the output of the extraction of SIFT features anddescriptors is a location (x,y) for each SIFT point 20 and a 128-valuedescriptor vector 30 associated with the SIFT point 20. The SIFT point20 locations and descriptor vectors 30 for all available reconnaissanceimages are stored in a database.

The SIFT points 20 and associated descriptor vectors 30 can be useddirectly to match a seeker image with a known target image, but in thisexample the number of target images is reduced by forming clusters ofviews. Specifically, one of the surveillance images is chosen as areference image and all the all of the other surveillance images arematched to that reference image (i.e. the SIFT points in eachsurveillance image are matched to the SIFT points in the referenceimage) (step A3). This process is repeated with other selected referenceimages. The reference images are selected so as to give distinct viewsof the target, e.g. views from front, rear and each side, with imagesthat provide views in intermediate directions being combined with thenearest reference image.

In the matching process (FIG. 3), which operates on the SIFT points 20and descriptors 30 of the surveillance images 100 (step 110), a pair ofimages are selected, being the reference image and one of the othersurveillance images. Each SIFT point in the reference image and eachSIFT point in the neighbouring view images is selected (steps 120, 130).Each SIFT point in the reference image is matched with every other pointin the neighbouring view and vice versa (step 140). The selected SIFTpoints are compared with each other by measuring the separation of theirdescriptor vectors, in a method described below. The method is repeatedfor all of the other SIFT points in the pair of images (loop 150), i.e.all of the SIFT points in the first image are compared with all of theSIFT points in the second image. The output of the process is a matrixG_(i,j) of SIFT point comparisons 160, with the (n, m)th entry in thematrix being the value arrived at by the comparison of the nth SIFTpoint in the first image with the mth SIFT point in the 2nd image.

The comparison between the two selected SIFT points is, in this example,a measurement of the Euclidean distance between their descriptorvectors, the distances being weighted by a Gaussian weighting function.As is well known in the art, the Euclidean distance between twothree-dimensional vectors is the square root of the sum of the squaresof the difference between corresponding components of the vectors, e.g.the distance between vectors

$x = {{\begin{pmatrix}x_{1} \\x_{2} \\x_{3}\end{pmatrix}\mspace{14mu} {and}\mspace{14mu} y} = {{\begin{pmatrix}y_{1} \\y_{2} \\y_{3}\end{pmatrix}\mspace{14mu} {is}\mspace{14mu} \sqrt{( {y - x} ) \cdot ( {y - x} )}} = \sqrt{\sum\limits_{i = 1}^{i = 3}\; ( {y_{i} - x_{i}} )^{2}}}}$

Applying a Gaussian weighting function has been found to give betterresults than a simple Euclidean distance for low resolution images. AGaussian weighting gives a higher weighting to vectors that arereasonably close together but a lower weighting to vectors that aresignificantly far apart. Thus, with the Gaussian weighting, the distancebetween the descriptor vectors is given by a proximity matrix:

$G_{i,j} = {\exp( \frac{- {\sum\limits_{k = 1}^{k = 128}\; ( {x_{j,k} - x_{i,k}} )^{2}}}{2\sigma^{2}} )}$

Where x_(j,k)−x_(i,k) is the difference between the kth component of thejth descriptor vector and the kth component of the ith descriptor vectorand σ is a parameter controlling the degree of interactions between thefeatures.

So G_(i,j) gives the weighted Euclidean distance between every pairingof SIFT point descriptor vectors. A good match exists where the distanceis small, in both directions (i.e. G_(i,j)≅G_(j,i) is small). Such goodmatches can be found by calculating the singular value decomposition(SVD) of the matrix, that is, factorising the matrix G=G_(i,j) asG=VDU^(T) where D is a diagonal matrix, and calculating a newcorrespondence matrix P by converting D to a companion matrix E whereeach diagonal element D_(i,i) is replaced with a 1 and P=VEU^(T). IfP_(i,j) is the largest element in its row and the largest element in itscolumn then there is regarded as being a one-to-one correspondencebetween the two features to which it relates, i.e. the ith feature inthe first image and the jth feature in the second image are declared tobe a good match.

This comparison thereby results in a list of matching SIFT points in thetwo images. The process of FIG. 3 is repeated for every pairing of thereference image with surveillance images (step 150).

Returning to FIG. 1, in the model-creation phase, next a view cluster isformed (step A4) by adding to the reference image all SIFT points thatare found in at least one other surveillance image and are in the fieldof view of the reference image but are not themselves in the referenceimage. To do that, for each pairing of a reference image with anadjacent surveillance image, the relationship between the views of theimages is established by calculating the similarity transform (i.e.translation, rotation and/or stretch about the image centre) that mapsthe location of SIFT points in the first image to the location of theSIFT points in the second image with a best fit. Such similaritytransforms can readily be calculated by the skilled person. The clusteris then formed by adding to the reference image any SIFT points fromsurveillance images that the similarity transform maps onto thereference image but that are not already on the reference image.

Thus, the set of surveillance images is reduced to a smaller set of keyreference images that have been enhanced by adding SIFT points from theother, non-reference, surveillance images. The seeker images can becompared with that reduced set of reference images, rather than all ofthe surveillance images, which reduces processing requirements, forexample in the missile, which as discussed above will typically haveonly limited resources.

A target feature model is formed (step A5) by collating the location anddescriptor vector information of the SIFT points in the referenceimages.

That completes the first phase of the method, which provides the targetfeature model.

When a target is sought by a missile, the missile's seeker generates asequence of images. Selected seeker images are matched to the referenceimages in the target feature model.

In a first step of that process, a missile seeker image is provided(step B1) SIFT features are located in the seeker image and descriptorvectors calculated (step B2), in the same way as is described above forthe surveillance images.

The seeker images are then matched to the reference images (step B3) by,for each reference image, calculating the distance between correspondingcomponents of the descriptor vectors for each pairing of SIFT pointsbetween the seeker and reference images. The distance is calculated asthe Gaussian weighted Euclidean distance (in the same way as describedabove for pairings of SIFT points between the surveillance images). Theresult is a matrix giving the distance of each SIFT point in the seekerimage from each SIFT point in the surveillance image. As before, goodmatches are found using SVD on the matrix to factorise the matrix andcalculating a new correspondence matrix. As before, the elements thatare largest in both their row and their column are regarded asindicating a one-to-one correspondence between the correspondingfeatures in the two images.

The result of that matching process is a list of features identified asbeing common to both the seeker image and the reference image beingprocessed. The next step is to estimate the location and pose of thetarget in the seeker image (step B4). It is almost inevitable that therewill be a significant number of mismatches between the seeker image andthe reference image, as there is typically a lot of data in thebackground of the seeker image, and so false matches are statisticallyvery likely. These accidental mismatches are excluded by testing thematches against a statistical test of significance, e.g. a Procrustesanalysis.

This method starts with two sets of points, the co-ordinates of matchedpoints in the seeker image and the reference image. The centroid of eachset is calculated, and the translation required to transform onecentroid to the other centroid is calculated, eliminating changes oftarget position between the images. For each image, the sum of thesquares of the distance of each point from the centroid is calculated,and each co-ordinate is divided by that number, eliminating any changein scale between the images. Finally, SVD is used to calculate thebest-fit rotation between the two sets of points, in a manner well knownto the skilled person. The similarity transform (translation, scalingand rotation) that best fits one set of points to the other is thusdetermined.

An error is calculated for each pair of matched SIFT points by applyingthe similarity transform to one of the pair of points. The distance ofthe (co-ordinates of the) transformed point from the other, matched,point of the pair is calculated. If the transformed point is close tothe matched point then the similarity transformation is a gooddescription of the relationship between the points; however, If thematching points in the two views cannot be related by a similaritytransform they are excluded from consideration as they are likely to bebackground points. Thus, pairs of points for which the error is largerthan a pre-selected threshold distance are discarded.

The remaining matched SIFT points are then used to assist detecting,locating and recognising the target in the seeker image. For example,the matched SIFT points can be highlighted in the seeker image as apotential target and presented to an operator or automatic targetidentification may be carried out before an operator takes a finaldecision as to the correct course of action.

While the present disclosure has been described and illustrated withreference to particular embodiments, it will be appreciated by those ofordinary skill in the art that the disclosure lends itself to manydifferent variations not specifically illustrated herein.

Where, in the foregoing description, integers or elements are mentionedthat have known, obvious, or foreseeable equivalents, then suchequivalents are herein incorporated as if individually set forth.Reference should be made to the claims for determining the true scope ofthe present disclosure, which should be construed so as to encompass anysuch equivalents. It will also be appreciated by the reader thatintegers or features of the disclosure that are described as optional donot limit the scope of the independent claims. Moreover, it is to beunderstood that such optional integers or features, while of possiblebenefit in some embodiments of the disclosure, may not be desirable, andcan therefore be absent, in other embodiments.

1. A method of targeting a missile, the method comprising: receiving aplurality of images of a target taken from a plurality of viewpoints;identifying in the images features characteristic of the target;providing data representing the characteristic features to the missileto enable the missile to identify, using the characteristic features,the target in images of the environment of the missile obtained from animager included in the missile.
 2. A method as claimed in claim 1,wherein the plurality of viewpoints are overlapping viewpoints.
 3. Amethod as claimed in claim 1, wherein the characteristic features areregions of the target which in the image of the target provide a changein contrast greater than a selected threshold value.
 4. A method asclaimed in claim 1, wherein the characteristic features are identifiedusing a scale-invariant feature transform algorithm.
 5. A method asclaimed in claim 1, wherein the identification of the characteristicfeatures includes the step of generating resealed versions of at leastone of the images of the target.
 6. A method as claimed in claim 5,wherein the identification of the characteristic features includes thestep of smoothing the resealed image versions.
 7. A method as claimed inclaim 6, wherein the identification of the characteristic featuresincludes the step of calculating difference images between the smoothed,resealed image versions.
 8. A method as claimed in claim 7, wherein theidentification of the characteristic features includes the step offinding extrema in the difference images.
 9. A method as claimed inclaim 8, wherein the identification of the characteristic featuresincludes the step of assigning an orientation to each extremum.
 10. Amethod as claimed in claim 8, wherein the identification of thecharacteristic features includes the step of generating a vectordescribing the extrema, comprising the orientation of the extrema.
 11. Amethod as claimed in claim 1, including the step of matchingcharacteristic features across two or more of the plurality of images.12. A method as claimed in claim 11, wherein the matching includesassessing the quality of the match against a statistical significancetest.
 13. A method as claimed in claim 1, including the step of forminga view cluster including characteristic features from two or more of thecorresponding images.
 14. A method as claimed in claim 13, including thestep of creating a model of the target from the characteristic featuresin the view clusters.
 15. A method as claimed in claim 1, wherein theimager included in the missile is a seeker.
 16. A method as claimed inclaim 1, including the step of identifying features characteristic ofthe target in the images of the environment of the missile.
 17. A methodas claimed in claim 16, including the step of matching thecharacteristic features in the other images of the environment of themissile to characteristic features in one or more of the images of thetarget or view clusters or a target model.
 18. A method as claimed inclaim 1, including the step of estimating the location and pose of thetarget in the images of the environment of the missile.
 19. A method oftargeting a missile, the method comprising: causing the missile toreceive data representing features characteristic of a target, thecharacteristic features having been identified in a plurality of imagesof the target taken from a plurality of viewpoints; the missileidentifying, using the characteristic features, the target in images ofthe environment of the missile obtained from an imager included in themissile.
 20. A missile comprising: a telecommunications receiver forreceiving data representing features characteristic of a target, thecharacteristic features having been identified in a plurality of imagesof the target taken from a plurality of viewpoints; an imager forobtaining images of the environment of the missile; a data processorconfigured to identify, using the characteristic features, the target inimages of the environment of the missile obtained from the imager.